Enhanced oil recovery operations are becoming increasingly more popular as reservoirs age and oil production declines. Waterflooding is by far the most widely used method, but it is sometimes economical to inject other fluids, such as hydrocarbon solvents, into a partially depleted oil field in an effort to recover oil which was not produced with waterflooding. When a solvent is used in an enhanced oil recovery operation, it is injected into the reservoir as a fluid which is miscible with the reservoir oil. This class of enhanced oil recovery is commonly known as a miscible flood because a miscible solvent is injected into the reservoir to mobilize and push the oil out of the reservoir.
Two different miscibility conditions can develop, depending on the solvent used and the reservoir conditions. The simplest and most direct method for achieving miscible displacement is to inject a solvent which completely mixes with the oil in all proportions when it first contacts the oil. This type of method produces mixtures of the solvent and oil in a single phase, and it is commonly called first contact miscible flooding. CO.sub.2, N.sub.2 and hydrocarbons of intermediate molecular weight, such as ethane propane, butane, or mixtures of LPG, are solvents that have been used most often for first contact miscible flooding.
If an operation uses a solvent which is not completely dissolved in the oil upon first contact, it is known as multiple contact miscible flooding. Since first contact miscible displacement is more effective than multiple contact miscible displacement in recovering oil, it is important to select a solvent composition to ensure the existence of first contact miscible conditions throughout the displacement process. The solvent composition and pressure necessary for miscibility can be determined from calculations, but constructing the necessary pseudoternary diagrams is time consuming and difficult to obtain experimentally.
In principle, the first contact miscible conditions can be determined by calculating vapor/liquid equilibria with appropriate equations of state or K-value correlations while concurrently mathematically simulating the multiple contacting and in situ mass transfer of components. However, this approach has several disadvantages. First, equations of state and K-value correlations are usually not sufficiently accurate in the region of interest. Therefore, the calibration of the correlations or equations of state must be made with the aid of experimental phase behavior data.
Another approach is to use available correlations of experimental miscibility data. However, some of these correlations are seriously in error, perhaps by 1000 psi or more. Correlations may be useful for purposes of screening reservoirs for suitability of miscible processes, but unless there is a large margin in operating pressure to allow for potential errors in the correlation estimates, miscibility pressure should be determined experimentally.
Flow experiments are preferred over calculations as a method for determining miscibility conditions. Selection of solvent composition and miscibility pressure is usually done in the laboratory using any one of a number of displacement techniques, e.g., slim-tube tests. Criteria for interpreting the displacements have included breakthrough and ultimate recoveries at a given volume of solvent injection, visual observations of core effluent, composition of produced gases, shape of the breakthrough and ultimate recovery curves vs. pressure, or combinations of these criteria. The different experimental techniques and interpretation criteria have led to vastly different conclusions.
Steps have been taken to increase the accuracy and precision of the experimental determinations. However, regardless of how accurate the laboratory work is, there is always a question as to whether the solvent determined to be first contact miscible in the laboratory will be first contact miscible with the oil in the reservoir. Since an accurate solvent design is essential to the success of an enhanced oil recovery project, it is highly desirable to use a technique which can monitor the miscibility in situ, i.e., in the reservoir, rather than in the laboratory.
Present in situ techniques for monitoring miscibility include the sampling and analysis of the produced hydrocarbon and gas. The process is determined to be first contact miscible if the gas/oil ratio and the compositions of the produced gas and hydrocarbon can be represented as a linear combination of reservoir oil and solvent. However, measurement errors in the gas/oil ratio and oil and gas compositions, combined with the inadequacy of the equations of state, make the results of this "recombination" technique inaccurate and ambiguous. In addition, this technique is not sufficiently sensitive to small changes in produced fluid properties, such as those which arise when the solvent and reservoir oil are slightly immiscible.
Other factors contribute to the inaccuracy of the "recombination" technique. The recombined reservoir oil may not truly represent the actual reservoir oil because of either improper sampling or the great variability of the oil and gas properties throughout the reservoir. This variability is particularly pronounced in reservoirs which had previously produced for an extended period of time by solution gas drive at below bubble point pressure followed by waterflooding in order to increase the reservoir pressure in preparation for the enhanced oil recovery project. When producing at below bubble point pressure for extended periods of time, the gas saturation, gas/oil ratio, and oil properties will be heterogeneous through the reservoir because of the vast differences in mobilities of gas and oil. Even where the reservoir is later pressurized above the bubble point, pockets of free gas will remain because the gas is slow to dissolve into the oil.
Consequently, there is still a need in the industry for an accurate method to monitor miscibility of a solvent in a miscible flood operation. The present invention, which is a direct, in situ method of monitoring miscibility fulfills this need because it is accurate and highly sensitive, and it requires simple and dependable data interpretation.